ADHD/ADD

Monitoring Amphetamine and Methamphetamine Mixtures Based on Deep Learning Involves Colorimetric Sensing.

Precise recognition and discrimination of highly similar analytes (either in structure or property) with distinguishable sensing responses are challenging but significant in the practical application of drug seizing, food additive inspection, environmental monitoring, etc. Here, a colorimetric differentiation strategy was proposed by modulating the probe structure to influence the aggregate behavi...

Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.

Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5%-7% of children worldwide. Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. This study aims to explore artificial intelligence models used for the prediction, early diagnosis and classifi...

Cerebrospinal fluid inflammatory cytokines as prognostic indicators for cognitive decline across Alzheimer's disease spectrum.

BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarker...

Functional connectivity anomalies in medication-naive children with ADHD: Diagnostic potential, symptoms interpretation, and a mediation model.

OBJECTIVE: To identify reliable electroencephalography (EEG) biomarkers for attention deficit/hyperactivity disorder (ADHD) by investigating anomalous...

Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.

Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in ...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a var...

ZnO nanoflower-mediated paper-based electrochemical biosensor for perfect classification of cardiac biomarkers with physics-informed machine learning.

The widespread exposure of acute myocardial infarction globally demands an ultrasensitive, rapid, and cost-effective biosensor for troponin-I and T in...

Automated ADHD detection using dual-modal sensory data and machine learning.

This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neur...

Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and Attention-Deficit/Hyperactivity Disorder With Psychological Test Reports.

BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability ...

Narrative Search Engine for Case Series Assessment Supported by Artificial Intelligence Query Suggestions.

INTRODUCTION: Manual identification of case narratives with specific relevant information can be challenging when working with large numbers of advers...

Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic age...

Is it necessary? A framework for assessing the utility of A.I. in HRM practices.

Artificial Intelligence is expected to be a value-adding intervention in HRM processes; however, there is still a large gap between its perception of ...

Author name disambiguation based on heterogeneous graph neural network.

With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguat...

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework.

In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-bei...

Better off alone? Artificial intelligence can demonstrate superior performance without clinician input.

Recent studies challenge the assumption that human-artificial intelligence (AI) collaboration is universally optimal, highlighting tasks where AI alon...

A systematic literature review of machine learning techniques for the detection of attention-deficit/hyperactivity disorder using MRI and/or EEG data.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition common in teenagers across the globe. Neuroimaging and Machine Learn...

Interpretable machine learning approaches for children's ADHD detection using clinical assessment data: an online web application deployment.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental disorder characterized by hyperactivity, impulsivity, and inattentio...

Carbon source dosage intelligent determination using a multi-feature sensitive back propagation neural network model.

The carbon reduction concept drives the development of low-carbon and sustainable wastewater treatment plant (WWTP) operation technologies. In the den...

Augmented Intelligence-Based Interference Pattern Analysis (AI-IPA) in Concentric Needle Electromyography.

INTRODUCTION/AIMS: To add objectivity to the routine needle electromyography examination, we describe an "Augmented Intelligence" based interference p...

A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD.

BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents characterized by persi...